JuriBERT: A Masked-Language Model Adaptation for French Legal Text

Language models have proven to be very useful when adapted to specific domains. Nonetheless, little research has been done on the adaptation of ___domain-specific BERT models in the French language. In this paper, we focus on creating a language model adapted to French legal text with the goal of helping law professionals. We conclude that some specific tasks do not benefit from generic language models pre-trained on large amounts of data. We explore the use of smaller architectures in ___domain-specific sub-languages and their benefits for French legal text. We prove that ___domain-specific pre-trained models can perform better than their equivalent generalised ones in the legal ___domain. Finally, we release JuriBERT, a new set of BERT models adapted to the French legal ___domain.